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Combination of blockade of endothelin signalling and compensation of IGF1 expression protects the retina from degeneration
Get PDFRetinitis pigmentosa (RP) and macular dystrophy (MD) cause severe retinal dysfunction, affecting 1 in 4000 people worldwide. This disease is currently assumed to be intractable, because effective therapeutic methods have not been established, regardless of genetic or sporadic traits. Here, we examined a RP mouse model in which the Prominin-1 (Prom1) gene was deficient and investigated the molecular events occurring at the outset of retinal dysfunction. We extracted the Prom1-deficient retina subjected to light exposure for a short time, conducted single-cell expression profiling, and compared the gene expression with and without stimuli. We identified the cells and genes whose expression levels change directly in response to light stimuli. Among the genes altered by light stimulation, Igf1 was decreased in rod photoreceptor cells and astrocytes under the light-stimulated condition. Consistently, the insulin-like growth factor (IGF) signal was weakened in light-stimulated photoreceptor cells. The recovery of Igf1 expression with the adeno-associated virus (AAV) prevented photoreceptor cell death, and its treatment in combination with the endothelin receptor antagonist led to the blockade of abnormal glial activation and the promotion of glycolysis, thereby resulting in the improvement of retinal functions, as assayed by electroretinography. We additionally demonstrated that the attenuation of mammalian/mechanistic target of rapamycin (mTOR), which mediates IGF signalling, leads to complications in maintaining retinal homeostasis. Together, we propose that combinatorial manipulation of distinct mechanisms is useful for the maintenance of the retinal condition.journal articl
The cellular protrusions for inter-cellular material transfer: similarities between filopodia, cytonemes, tunneling nanotubes, viruses, and extracellular vesicles
No full textExtracellular vesicles (EVs) are crucial for transferring bioactive materials between cells and play vital roles in both health and diseases. Cellular protrusions, including filopodia and microvilli, are generated by the bending of the plasma membrane and are considered to be rigid structures facilitating various cellular functions, such as cell migration, adhesion, and environment sensing. Compelling evidence suggests that these protrusions are dynamic and flexible structures that can serve as sources of a new class of EVs, highlighting the unique role they play in intercellular material transfer. Cytonemes are specialized filopodia protrusions that make direct contact with neighboring cells, mediating the transfer of bioactive materials between cells through their tips. In some cases, these tips fuse with the plasma membrane of neighboring cells, creating tunneling nanotubes that directly connect the cytosols of the adjacent cells. Additionally, virus particles can be released from infected cells through small bud-like of plasma membrane protrusions. These different types of protrusions, which can transfer bioactive materials, share common protein components, including I-BAR domain-containing proteins, actin cytoskeleton, and their regulatory proteins. The dynamic and flexible nature of these protrusions highlights their importance in cellular communication and material transfer within the body, including development, cancer progression, and other diseases.journal articl
CLIP feature-based randomized control using images and text for multiple tasks and robots
No full textThis study presents a control framework leveraging vision language models (VLMs) for multiple tasks and robots. Notably, existing control methods using VLMs have achieved high performance in various tasks and robots in the training environment. However, these methods incur high costs for learning control policies for tasks and robots other than those in the training environment. Considering the application of industrial and household robots, learning in novel environments where robots are introduced is challenging. To address this issue, we propose a control framework that does not require learning control policies. Our framework combines the vision-language CLIP model with a randomized control. CLIP computes the similarity between images and texts by embedding them in the feature space. This study employs CLIP to compute the similarity between camera images and text representing the target state. In our method, the robot is controlled by a randomized controller that simultaneously explores and increases the similarity gradients. Moreover, we fine-tune the CLIP to improve the performance of the proposed method. Consequently, we confirm the effectiveness of our approach through a multitask simulation and a real robot experiment using a two-wheeled robot and robot arm.journal articl
Visual Question Answering ニ オケル シセン ジョウホウ オ モチイタ シツモン ノ アイマイセイ カイショウ
Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Comparative genomics for arbuscular mycorrhizal symbiosis in parasitic plants
Get PDF奈良先端科学技術大学院大学博士（バイオサイエンス）doctoral thesi
アブラナカ ショクブツ ノ ジカ フワゴウ ニオケル カフン インシ ノ ケンセイ センセイ セイギョ ノ ブンシ キコウ オヨビ シュカン ヒカク カイセキ
Get PDF奈良先端科学技術大学院大学博士（バイオサイエンス）doctoral thesi
Identification and Functional Analysis of An Exo-Acting PET Hydrolytic Enzyme of the PET-degrading Bacterium, Ideonella sakaiensis
Get PDF奈良先端科学技術大学院大学博士（バイオサイエンス）doctoral thesi
Pyrosilicate single crystals for ionizing radiation detection
Get PDF奈良先端科学技術大学院大学博士（工学）doctoral thesi
Optimization of Distributed SDN Controller Placement Considering East-West Interface Overhead
Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders
No full textExperienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.journal articl